Securing AI use with a lean security team

The map

By the time anyone asks whether the company's AI use is secure, AI is already inside the company: people paste text into chatbots, and someone has connected an AI notetaker to the calendar. The CEO wants to hear that all of it is safe, and the team fielding the question is three or five people who already run the whole security program, with no committee or in-house counsel to hand any of it to. In RH-ISAC's 2026 CISO benchmark (a retail and hospitality panel, published TLP:CLEAR), 70% of CISOs said AI had been added to their responsibilities, and 71% ranked AI as their top friction point, above ransomware and phishing.

The public frameworks are good at naming what can go wrong: the NIST AI Risk Management Framework, the OWASP Top 10 for LLM Applications, the EU AI Act. None of them tells you which products cover which risk.

This guide is that map: five problems, each with a clear scope and a market category behind it. The tools that follow will get your team ready to approach the right vendors with the right questions.

1 · Shadow AI

Every control downstream depends on knowing what people actually run. Three sources cover most of it: expense reports, where AI subscriptions look like ordinary software charges; SSO and OAuth logs, where every sign-in and data grant leaves a record; and asking colleagues directly, framed as an amnesty so that naming a tool carries no punishment.

A manual inventory decays fast, and keeping it current is the first thing worth buying. AI Usage Control products discover third-party AI whether it is consumed as a service, installed locally, or embedded inside other applications, and then enforce usage policies over it: content inspection for sensitive data, a risk assessment per tool, and alerts when usage patterns change.

2 · Data flowing into models

With the inventory in hand, the next three problems are control problems. One rule before buying for any of them: extend the controls you already have for identity, data, and applications wherever they fit, and buy AI-specific tooling only where AI creates a gap nothing existing covers.

LLM Security products protect the prompt and response pipeline of generative applications: inline guardrails that block prompt injection, filter what models return, and stop sensitive data from leaving, with model access control and audit logging behind them.

3 · Agents acting on their own

An agent is a non-human actor holding credentials, which makes it a machine identity problem. Machine Identity Management products handle issuance, rotation, and governance for keys, secrets, service accounts, and workload identities. Short-lived credentials make an autonomous tool revocable, and therefore approvable.

4 · Untested attack surface

AI Red Teaming products run deliberate attack campaigns against LLMs, agents, and multimodal models. They look for prompt injection, jailbreaks, data leakage, guardrail bypasses, and agentic exploit paths, and they map findings to the OWASP LLM Top 10 and MITRE ATLAS. This is the pass a lean team rarely has time to script by hand.

Two adjacent categories matter mostly when you build AI products yourself: AI Model Protection defends the model artifact against extraction, inversion, and adversarial attack, and AI Security Posture Management assesses the configurations, access, and data flows of your AI assets. Knowing where these boundaries sit keeps vendor conversations precise, because plenty of pitches blur these categories into "AI security."

5 · Governance and audit evidence

The question that opened this guide is answered with records, whoever asks it next: an auditor, a customer running a security review, a regulator, or a board once there is one. There is a category for producing those records. AI Governance Platforms keep the AI inventory and catalog, apply risk frameworks such as the NIST AI RMF and the EU AI Act, check policy compliance at runtime, and collect the evidence an audit will ask for. They are built for whoever owns AI governance and assurance, and at a fifty-person company that owner is usually also the security team. Certification is drifting the same direction: ISO/IEC 42001, the AI management standard, is starting to appear on vendor trust pages.

None of this requires a standing AI committee or a forty-page policy. One named owner with the authority to say no, a policy short enough that people read it, and a spare column in the existing risk register cover the governance layer at this scale. There is no need to wait for regulatory clarity either: the EU AI Act obligations are published with dates, so start now and adjust footnotes later.

From gap to procurement

The map above aligns your problem with the right category of products, useful in a market where new AI security tools crop up by the day. The category tells you where to look, but you'll find the best match through the exact capabilities you require.

The requirements builder in the Security Stack workspace takes you from a known gap to the first vendor call in four steps:

1. Describe the need. Pick the AI Security evaluation pack and select outcomes like the ones in this guide, each phrased as something a vendor must demonstrate and flagged as a "Must have" or "Nice to have".
2. Set your constraints. Compliance certifications, required integrations, and deployment model narrow the field before any call happens.
3. Get a ranked shortlist. The builder will score each candidate against researcher-reviewed documentation of its features, and rank them based on that evidence.
4. Export the brief. The result is a procurement-ready document: your outcomes, each product's evidence, and the gaps to probe in the first vendor call.

Expect the budget to come from somewhere painful. In the same RH-ISAC panel, roughly 90% of CISOs expected AI security spending to be funded by cutting something else, and the inventory you built first doubles as the funding search: every overlapping or unused subscription it surfaces is money you can reclaim at renewal.

Start with your most pressing problem, and take the first vendor call with the brief in hand.

Sources

• RH-ISAC CISO Benchmark 2026 (TLP:CLEAR), the source for the 70%, 71%, and 90% figures.
• NIST AI Risk Management Framework and its Generative AI Profile.
• OWASP Top 10 for LLM Applications.
• MITRE ATLAS, the adversarial threat matrix for AI systems.
• Regulation (EU) 2024/1689 (the EU AI Act).
• ISO/IEC 42001, AI management systems.